Find most focal recurrent copy number units

Rscript -e "rmarkdown::render('analyses/focal-cn-file-preparation/05-define-most-focal-cn-units.Rmd', clean = TRUE)"

# The percentage of calls a particular status needs to be 
# above to be called the majority status -- the decision
# for a cutoff of 90% here was made to ensure that the status
# is not only the majority status but it is also significantly
# called more than the other status values in the region
percent_threshold <- 0.9

# The percentage threshold for determining if enough of a region
# (arm, cytoband, or gene) is callable to determine its status --
# the decision for a cutoff of 50% here was made as it seems reasonable
# to expect a region to be more than 50% callable for a dominant status
# call to be made
uncallable_threshold <- 0.5

library(tidyverse)

status_majority_caller <- function(status_df,
                                   region_variable,
                                   status_column_name,
                                   percent_threshold_value, 
                                   uncallable_threshold_value = uncallable_threshold) {
  # Given a data.frame with cytoband/gene-level copy number status data,
  # find the dominant status of the cytoband/gene region by calculating
  # the percentages of the region that each call represents.
  #
  # Args:
  #   status_df: data.frame with cytoband/gene-level copy number status data
  #   region_variable: string of the column name/region to calculate copy number
  #                    status percentages for
  #   status_column_name: string of the column name that holds the relevant copy
  #                       number status data
  #   percent_threshold_value: What percent of calls a particular status needs to be 
  #                      above to be called the majority. 
  #   uncallable_threshold_value: a threshold for determining if enough of a region is 
  #                         callable to determine its status. 
  #   
  # Return:
  #   status_count: data.frame with percentage values for each unique status in
  #                 each unique region (arm/cytoband/gene) and the dominant
  #                 status for that region
  
  # Tidyeval for these columns 
  region_sym <- rlang::sym(region_variable)
  status_sym <- rlang::sym(status_column_name)
  
  # Format the data and group it
  status_count <- status_df %>%
    count(!!region_sym, !!status_sym) %>%
    # Spread the data -- each row represents a unique chromosome arm
    spread(!!status_sym, n) %>%
    # Turn NAs into 0s
    replace_na(list(
      gain = 0,
      loss = 0,
      neutral = 0,
      amplification = 0,
      uncallable = 0,
      unstable = 0
    )) %>%
    # Getting counts by region 
    group_by(!!region_sym) 
  
  # Let's store the regions separately so as to avoid weird coercions
  region_vector <- 
    status_count %>% 
    dplyr::pull(!!region_sym)
  
  # Calculate percent 
  status_count <- status_count %>%
    # Store region variable column out of the way as rownames
    tibble::column_to_rownames(region_variable) %>% 
    # Obtain a total counts variable column
    dplyr::mutate(total = apply(., 1, sum)) %>% 
    # Get the ratio of each status count to the total
    dplyr::mutate_at(dplyr::vars(-total), dplyr::funs(. / total)) %>% 
    # Bring back our region variable as its own column
    dplyr::mutate(!!region_variable := region_vector)

  # The logic here is to define the region status based on the majority of calls
  # in the region -- if the number of calls for a specific status is 
  # responsible for more than `percent_threshold` value of the total calls in that
  # region, then it is used to define the region's status (exception is for the 
  # uncallable status where we define the region as uncallable when more than the
  # `uncallable_threshold` of the calls in that region are uncallable)
  if ((region_variable == "chromosome_arm") | (region_variable == "gene_symbol")) {
    status_count <- status_count %>%
      mutate(
        dominant_status = case_when(
          gain > percent_threshold ~ "gain",
          loss > percent_threshold ~ "loss",
          amplification > percent_threshold ~ "amplification",
          TRUE ~ "neutral"
        )
      )
  } else if (region_variable == "cytoband") {
    status_count <- status_count %>%
      mutate(
        dominant_status = case_when(
          uncallable > uncallable_threshold ~ "uncallable",
          gain > percent_threshold~ "gain",
          loss > percent_threshold ~ "loss",
          neutral > percent_threshold ~ "neutral",
          TRUE ~ "unstable"
        )
      )
  }

  return(status_count)
}

plot_dominant_status_calls <- function(status_count_df,
                                       region_variable) {
  # Given a data.frame with the percentage values for each region and the
  # dominant status for that region, plot the dominant status call on the
  # x-axis with the percentage values on the y-axis.
  #
  # Args:
  #   status_count_df: data.frame with percentage values for each unique status
  #                    in each unique region (arm/cytoband/gene) and the
  #                    dominant status for that region
  #   region_variable: string of the region (which will also be a column name)
  #                    that the data.frame holds percentage values for
  # Return:
  #   status_plot: plot representing the dominant status call on the x-axis and
  #                the percentage values on the y-axis
  
  status_count_df %>%
    # Remove the total column -- we don't want to plot this
    select(-total) %>%
    dplyr::ungroup() %>%
    # Store the non-percentage value column variable as rownames
    tibble::column_to_rownames(region_variable) %>%
    # Gather the data.frame to have columns and values in the format of
    # our status call, the percentage of total calls that status call is
    # responisble for, and the dominant status call made based on the
    # percentage value
    tidyr::gather(status, percent,-dominant_status) %>%
    # Plot our focal status values on the x-axis and the percentage values
    # on the y-axis
    ggplot2::ggplot(ggplot2::aes(x = status,
                                 y = percent)) +
    ggplot2::geom_jitter() +
    # Facet wrap around each dominant status value
    ggplot2::facet_wrap( ~ dominant_status)
}

results_dir <- "results"

# Define a logical object for running in CI
running_in_ci <- params$is_ci

# Read in the file with consensus CN status data and the UCSC cytoband data --
# generated in `03-add-cytoband-status-consensus.Rmd`
consensus_seg_cytoband_status_df <-
  read_tsv(file.path("results", "consensus_seg_with_ucsc_cytoband_status.tsv.gz")) %>%
  # Need this to not have `chr`
  mutate(chr = gsub("chr", "", chr),
         cytoband = paste0(chr, cytoband)) %>%
  select(
    chromosome_arm,
    # Distinguish this dominant status that is based on cytobands, from the status 
    dominant_cytoband_status = dominant_status,
    cytoband,
    Kids_First_Biospecimen_ID
  )

Parsed with column specification:
cols(
  Kids_First_Biospecimen_ID = col_character(),
  chr = col_character(),
  cytoband = col_character(),
  dominant_status = col_character(),
  band_length = col_double(),
  callable_fraction = col_double(),
  gain_fraction = col_double(),
  loss_fraction = col_double(),
  chromosome_arm = col_character()
)

# Read in the annotated CN file (without the UCSC data)
consensus_seg_autosomes_df <-
  read_tsv(file.path(results_dir, "consensus_seg_annotated_cn_autosomes.tsv.gz"))

Parsed with column specification:
cols(
  biospecimen_id = col_character(),
  status = col_character(),
  copy_number = col_double(),
  ploidy = col_double(),
  ensembl = col_character(),
  gene_symbol = col_character(),
  cytoband = col_character()
)

combined_status_df <- consensus_seg_autosomes_df %>%
  inner_join(
    consensus_seg_cytoband_status_df,
    by = c("biospecimen_id" = "Kids_First_Biospecimen_ID", "cytoband")
  )

# Use our custom function to make the status calls
arm_status_count <- status_majority_caller(
  combined_status_df,
  "chromosome_arm",
  "status", 
  percent_threshold = percent_threshold
)

# Display table
arm_status_count %>%
  group_by(dominant_status)

# Let's get a vector of the neutral arms
neutral_arms <- arm_status_count %>%
  filter(dominant_status == "neutral") %>% 
  dplyr::pull("chromosome_arm")

# Filter the annotated CN data to include only neutral chromosome arms
neutral_status_arm_df <- combined_status_df %>%
  filter(chromosome_arm %in% neutral_arms)

if (!(running_in_ci)) {
# Now count the cytoband level calls (for each status call) and define
# the cytoband as that status if more than 50% of the total counts are
# for that particular status
cytoband_status_count <- status_majority_caller(
  neutral_status_arm_df,
  "cytoband",
  "dominant_cytoband_status",
  percent_threshold = percent_threshold
)

# Display table
cytoband_status_count
}

if (!(running_in_ci)) {
  # These are the cytobands that have not been defined as gain or loss --
  # we want to define their gene-level status
  neutral_cytobands <- cytoband_status_count %>%
    filter(dominant_status %in% c("unstable", "neutral")) %>%
    dplyr::pull("cytoband")

  # Filter the annotated CN data to include only these cytobands
  neutral_status_cytoband_df <- combined_status_df %>%
    filter(cytoband %in% neutral_cytobands)
  
  # Now count the gene-level calls (for each status call) and define
  # the gene as that status if more than 50% of the total counts are
  # for that particular status
  gene_status_count <- status_majority_caller(neutral_status_cytoband_df,
                                              "gene_symbol",
                                              "status", 
                                              percent_threshold = percent_threshold)
  
  # Display table
  gene_status_count
}

# Run `plot_dominant_status_calls` function for the chromosome arm calls
plot_dominant_status_calls(
  arm_status_count,
  "chromosome_arm"
)

# Run `plot_dominant_status_calls` function for the cytoband calls if not
# running in CI
if (!(running_in_ci)) {
  plot_dominant_status_calls(cytoband_status_count,
                             "cytoband")
}

# Plot if not running in circleCI
if (!(running_in_ci)) {
  plot_dominant_status_calls(gene_status_count,
                             "gene_symbol")
}

# The logic variable `running_in_ci` is needed here because the CI testing
# files do not contain any of the genes in the `gene_status_count` data.frame
# generated above (when `running_in_ci` == FALSE)
if (!(running_in_ci)) {
  final_df <- consensus_seg_autosomes_df %>%
    mutate(chromosome_arm = gsub("(p|q).*", "\\1", cytoband)) %>%
    inner_join(arm_status_count,
               by = "chromosome_arm") %>%
    left_join(cytoband_status_count,
              by = "cytoband",
              suffix = c(".arm", ".cytoband")) %>%
    left_join(gene_status_count,
              by = "gene_symbol",
              suffix = c(".arm", ".gene")) %>%
    mutate(
      focal_call = paste0(
        dominant_status.arm,
        ", ",
        dominant_status.cytoband,
        ", ",
        dominant_status
      ),
      # Here we want to define the most focal call based on the arm, cytoband,
      # and gene status information -- if a loss/gain is defined at the arm
      # level then the focal call will be "arm_loss" or "arm_gain" respectively,
      # and so on.
      focal_call = case_when(focal_call == "loss, NA, NA" ~ "arm_loss",
                             focal_call == "neutral, uncallable, NA" ~ "uncallable",
                             focal_call == "neutral, loss, NA" ~ "cytoband_loss",
                             focal_call == "neutral, NA, NA" ~ "arm_neutral",
                             focal_call == "neutral, unstable, neutral" ~ "gene_neutral",
                             focal_call == "neutral, unstable, loss" ~ "gene_loss",
                             TRUE ~ "Other")
    )
} else {
  final_df <- consensus_seg_autosomes_df %>%
    mutate(chromosome_arm = gsub("(p|q).*", "\\1", cytoband)) %>%
    inner_join(arm_status_count, by = "chromosome_arm") %>%
    mutate(focal_call = dominant_status)
}

# Display final table
final_df %>%
  arrange(focal_call) %>%
  group_by(biospecimen_id, focal_call)

if (!(running_in_ci)) {
  final_arm_status_df <- final_df %>%
    # Filter to only non-neutral chromosome arms
    filter(dominant_status.arm != "neutral") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = chromosome_arm,
      status = dominant_status.arm
    ) %>%
    distinct() %>%
    mutate(region_type = "chromosome_arm")
} else {
  final_arm_status_df <- final_df %>%
    # Filter to only non-neutral chromosome arms
    filter(dominant_status != "neutral") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = chromosome_arm,
      status = dominant_status
    ) %>%
    distinct() %>%
    mutate(region_type = "chromosome_arm")
}

if (!(running_in_ci)) {
  final_cytoband_status_df <- final_df %>%
    # Filter to only neutral chromosome arms and cytobands
    # that are not NA
    filter(dominant_status.arm == "neutral",
           dominant_status.cytoband != "NA") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = cytoband,
      status = dominant_status.cytoband
    ) %>%
    distinct() %>%
    mutate(region_type = "cytoband")
}

if (!(running_in_ci)) {
  final_gene_status_df <- final_df %>%
    # Filter to only neutral chromosome arms and cytoband
    # that are NA
    filter(dominant_status.arm == "neutral",
           dominant_status.cytoband == "NA") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = gene_symbol,
      status = dominant_status
    ) %>%
    distinct() %>%
    mutate(region_type = "gene_symbol")
}

if (!(running_in_ci)) {
  # Bind the rows of each region's data.frame
  final_long_status_df <- bind_rows(final_arm_status_df,
                                    final_cytoband_status_df,
                                    final_gene_status_df) %>%
    filter(status != "uncallable")
} else {
  final_long_status_df <- final_arm_status_df %>%
    filter(status != "uncallable")
}

# Write final long status table to file
write_tsv(final_long_status_df, file.path(results_dir, "consensus_seg_most_focal_cn_status.tsv.gz"))

# Display final long status table
final_long_status_df

final_wide_status_df <- final_long_status_df %>%
  tidyr::spread(Kids_First_Biospecimen_ID, status)

# Display status data spread across samples in wide format -- each row is a
# unique region
final_wide_status_df

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 9 (stretch)

Matrix products: default
BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.0.0.9000 forcats_0.4.0        stringr_1.4.0        dplyr_0.8.3         
 [5] purrr_0.3.2          readr_1.3.1          tidyr_0.8.3          tibble_2.1.3        
 [9] ggplot2_3.2.0        tidyverse_1.2.1     

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1       cellranger_1.1.0 pillar_1.4.2     compiler_3.6.0   base64enc_0.1-3  tools_3.6.0     
 [7] zeallot_0.1.0    digest_0.6.20    evaluate_0.14    jsonlite_1.6     lubridate_1.7.4  nlme_3.1-140    
[13] gtable_0.3.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[19] yaml_2.2.0       haven_2.1.1      xfun_0.8         withr_2.1.2      xml2_1.2.0       httr_1.4.0      
[25] knitr_1.23       vctrs_0.1.0      generics_0.0.2   hms_0.4.2        grid_3.6.0       tidyselect_0.2.5
[31] glue_1.3.1       R6_2.4.0         fansi_0.4.0      readxl_1.3.1     rmarkdown_1.13   modelr_0.1.4    
[37] magrittr_1.5     backports_1.1.4  scales_1.0.0     htmltools_0.3.6  rsconnect_0.8.13 rvest_0.3.4     
[43] assertthat_0.2.1 colorspace_1.4-1 labeling_0.3     utf8_1.1.4       stringi_1.4.3    lazyeval_0.2.2  
[49] munsell_0.5.0    broom_0.5.2      crayon_1.3.4    

---
title: "Find most focal recurrent copy number units"
output: 
  html_notebook:
    toc: TRUE
    toc_float: TRUE
author: Chante Bethell for ALSF CCDL
date: 2020
params:
  is_ci: FALSE
---

This notebook defines the most focal recurrent copy number units by removing focal changes that are within entire chromosome arm losses and gains.
_Most focal_ here meaning:

- If a chromosome arm is not clearly defined as a gain or loss (and is callable) we look to define the cytoband level status
- If a cytoband is not clearly defined as a gain or loss (and is callable) we then look to define the gene-level status

## Usage

This notebook is intended to be run from the command line with the following (assumes you are in the root directory of the repository):

```
Rscript -e "rmarkdown::render('analyses/focal-cn-file-preparation/05-define-most-focal-cn-units.Rmd', clean = TRUE)"
```

### Cutoffs: 

```{r}
# The percentage of calls a particular status needs to be 
# above to be called the majority status -- the decision
# for a cutoff of 90% here was made to ensure that the status
# is not only the majority status but it is also significantly
# called more than the other status values in the region
percent_threshold <- 0.9

# The percentage threshold for determining if enough of a region
# (arm, cytoband, or gene) is callable to determine its status --
# the decision for a cutoff of 50% here was made as it seems reasonable
# to expect a region to be more than 50% callable for a dominant status
# call to be made
uncallable_threshold <- 0.5
```

## Set up

### Libraries and functions

```{r}
library(tidyverse)
```

### Custom Function

```{r}
status_majority_caller <- function(status_df,
                                   region_variable,
                                   status_column_name,
                                   percent_threshold_value, 
                                   uncallable_threshold_value = uncallable_threshold) {
  # Given a data.frame with cytoband/gene-level copy number status data,
  # find the dominant status of the cytoband/gene region by calculating
  # the percentages of the region that each call represents.
  #
  # Args:
  #   status_df: data.frame with cytoband/gene-level copy number status data
  #   region_variable: string of the column name/region to calculate copy number
  #                    status percentages for
  #   status_column_name: string of the column name that holds the relevant copy
  #                       number status data
  #   percent_threshold_value: What percent of calls a particular status needs to be 
  #                      above to be called the majority. 
  #   uncallable_threshold_value: a threshold for determining if enough of a region is 
  #                         callable to determine its status. 
  #   
  # Return:
  #   status_count: data.frame with percentage values for each unique status in
  #                 each unique region (arm/cytoband/gene) and the dominant
  #                 status for that region
  
  # Tidyeval for these columns 
  region_sym <- rlang::sym(region_variable)
  status_sym <- rlang::sym(status_column_name)
  
  # Format the data and group it
  status_count <- status_df %>%
    count(!!region_sym, !!status_sym) %>%
    # Spread the data -- each row represents a unique chromosome arm
    spread(!!status_sym, n) %>%
    # Turn NAs into 0s
    replace_na(list(
      gain = 0,
      loss = 0,
      neutral = 0,
      amplification = 0,
      uncallable = 0,
      unstable = 0
    )) %>%
    # Getting counts by region 
    group_by(!!region_sym) 
  
  # Let's store the regions separately so as to avoid weird coercions
  region_vector <- 
    status_count %>% 
    dplyr::pull(!!region_sym)
  
  # Calculate percent 
  status_count <- status_count %>%
    # Store region variable column out of the way as rownames
    tibble::column_to_rownames(region_variable) %>% 
    # Obtain a total counts variable column
    dplyr::mutate(total = apply(., 1, sum)) %>% 
    # Get the ratio of each status count to the total
    dplyr::mutate_at(dplyr::vars(-total), dplyr::funs(. / total)) %>% 
    # Bring back our region variable as its own column
    dplyr::mutate(!!region_variable := region_vector)

  # The logic here is to define the region status based on the majority of calls
  # in the region -- if the number of calls for a specific status is 
  # responsible for more than `percent_threshold` value of the total calls in that
  # region, then it is used to define the region's status (exception is for the 
  # uncallable status where we define the region as uncallable when more than the
  # `uncallable_threshold` of the calls in that region are uncallable)
  if ((region_variable == "chromosome_arm") | (region_variable == "gene_symbol")) {
    status_count <- status_count %>%
      mutate(
        dominant_status = case_when(
          gain > percent_threshold ~ "gain",
          loss > percent_threshold ~ "loss",
          amplification > percent_threshold ~ "amplification",
          TRUE ~ "neutral"
        )
      )
  } else if (region_variable == "cytoband") {
    status_count <- status_count %>%
      mutate(
        dominant_status = case_when(
          uncallable > uncallable_threshold ~ "uncallable",
          gain > percent_threshold~ "gain",
          loss > percent_threshold ~ "loss",
          neutral > percent_threshold ~ "neutral",
          TRUE ~ "unstable"
        )
      )
  }

  return(status_count)
}
```

```{r}
plot_dominant_status_calls <- function(status_count_df,
                                       region_variable) {
  # Given a data.frame with the percentage values for each region and the
  # dominant status for that region, plot the dominant status call on the
  # x-axis with the percentage values on the y-axis.
  #
  # Args:
  #   status_count_df: data.frame with percentage values for each unique status
  #                    in each unique region (arm/cytoband/gene) and the
  #                    dominant status for that region
  #   region_variable: string of the region (which will also be a column name)
  #                    that the data.frame holds percentage values for
  # Return:
  #   status_plot: plot representing the dominant status call on the x-axis and
  #                the percentage values on the y-axis
  
  status_count_df %>%
    # Remove the total column -- we don't want to plot this
    select(-total) %>%
    dplyr::ungroup() %>%
    # Store the non-percentage value column variable as rownames
    tibble::column_to_rownames(region_variable) %>%
    # Gather the data.frame to have columns and values in the format of
    # our status call, the percentage of total calls that status call is
    # responisble for, and the dominant status call made based on the
    # percentage value
    tidyr::gather(status, percent,-dominant_status) %>%
    # Plot our focal status values on the x-axis and the percentage values
    # on the y-axis
    ggplot2::ggplot(ggplot2::aes(x = status,
                                 y = percent)) +
    ggplot2::geom_jitter() +
    # Facet wrap around each dominant status value
    ggplot2::facet_wrap( ~ dominant_status)
}
```


### Files and directories

```{r}
results_dir <- "results"

# Define a logical object for running in CI
running_in_ci <- params$is_ci
```

### Read in files

Read in cytoband status file and format it for what we will need in this notebook. 

```{r}
# Read in the file with consensus CN status data and the UCSC cytoband data --
# generated in `03-add-cytoband-status-consensus.Rmd`
consensus_seg_cytoband_status_df <-
  read_tsv(file.path("results", "consensus_seg_with_ucsc_cytoband_status.tsv.gz")) %>%
  # Need this to not have `chr`
  mutate(chr = gsub("chr", "", chr),
         cytoband = paste0(chr, cytoband)) %>%
  select(
    chromosome_arm,
    # Distinguish this dominant status that is based on cytobands, from the status 
    dominant_cytoband_status = dominant_status,
    cytoband,
    Kids_First_Biospecimen_ID
  )
```

Read in the chromosome-level and gene-level data. 

```{r}
# Read in the annotated CN file (without the UCSC data)
consensus_seg_autosomes_df <-
  read_tsv(file.path(results_dir, "consensus_seg_annotated_cn_autosomes.tsv.gz"))
```

Joining the gene-level, cytoband-level, and arm-level data into one data frame.

```{r}
combined_status_df <- consensus_seg_autosomes_df %>%
  inner_join(
    consensus_seg_cytoband_status_df,
    by = c("biospecimen_id" = "Kids_First_Biospecimen_ID", "cytoband")
  )
```

## Define most focal units

### Determine chromosome arm status

```{r}
# Use our custom function to make the status calls
arm_status_count <- status_majority_caller(
  combined_status_df,
  "chromosome_arm",
  "status", 
  percent_threshold = percent_threshold
)

# Display table
arm_status_count %>%
  group_by(dominant_status)
```

These are the chromosome arms that have not been defined as gain or loss -- we want to define their cytoband/gene-level status

```{r}
# Let's get a vector of the neutral arms
neutral_arms <- arm_status_count %>%
  filter(dominant_status == "neutral") %>% 
  dplyr::pull("chromosome_arm")
```

### Determine cytoband status

We want to include cytoband and gene-level calls for chromosome arms that have not been defined as a gain or loss.

```{r}
# Filter the annotated CN data to include only neutral chromosome arms
neutral_status_arm_df <- combined_status_df %>%
  filter(chromosome_arm %in% neutral_arms)
```

Making cytoband-level majority calls. 

```{r}
if (!(running_in_ci)) {
# Now count the cytoband level calls (for each status call) and define
# the cytoband as that status if more than 50% of the total counts are
# for that particular status
cytoband_status_count <- status_majority_caller(
  neutral_status_arm_df,
  "cytoband",
  "dominant_cytoband_status",
  percent_threshold = percent_threshold
)

# Display table
cytoband_status_count
}
```

### Determine gene-level status

```{r}
if (!(running_in_ci)) {
  # These are the cytobands that have not been defined as gain or loss --
  # we want to define their gene-level status
  neutral_cytobands <- cytoband_status_count %>%
    filter(dominant_status %in% c("unstable", "neutral")) %>%
    dplyr::pull("cytoband")

  # Filter the annotated CN data to include only these cytobands
  neutral_status_cytoband_df <- combined_status_df %>%
    filter(cytoband %in% neutral_cytobands)
  
  # Now count the gene-level calls (for each status call) and define
  # the gene as that status if more than 50% of the total counts are
  # for that particular status
  gene_status_count <- status_majority_caller(neutral_status_cytoband_df,
                                              "gene_symbol",
                                              "status", 
                                              percent_threshold = percent_threshold)
  
  # Display table
  gene_status_count
}
```

## Plot calls

Plot the final dominant status call on the x-axis and the percent of each status on the y-axis.

### Plot chromosome arm status calls

```{r}
# Run `plot_dominant_status_calls` function for the chromosome arm calls
plot_dominant_status_calls(
  arm_status_count,
  "chromosome_arm"
)
```

### Plot cytoband status calls

```{r fig.width = 10}
# Run `plot_dominant_status_calls` function for the cytoband calls if not
# running in CI
if (!(running_in_ci)) {
  plot_dominant_status_calls(cytoband_status_count,
                             "cytoband")
}
```

### Plot gene status calls

```{r}
# Plot if not running in circleCI
if (!(running_in_ci)) {
  plot_dominant_status_calls(gene_status_count,
                             "gene_symbol")
}
```

## Combine arm, cytoband, and gene-level status data

```{r}
# The logic variable `running_in_ci` is needed here because the CI testing
# files do not contain any of the genes in the `gene_status_count` data.frame
# generated above (when `running_in_ci` == FALSE)
if (!(running_in_ci)) {
  final_df <- consensus_seg_autosomes_df %>%
    mutate(chromosome_arm = gsub("(p|q).*", "\\1", cytoband)) %>%
    inner_join(arm_status_count,
               by = "chromosome_arm") %>%
    left_join(cytoband_status_count,
              by = "cytoband",
              suffix = c(".arm", ".cytoband")) %>%
    left_join(gene_status_count,
              by = "gene_symbol",
              suffix = c(".arm", ".gene")) %>%
    mutate(
      focal_call = paste0(
        dominant_status.arm,
        ", ",
        dominant_status.cytoband,
        ", ",
        dominant_status
      ),
      # Here we want to define the most focal call based on the arm, cytoband,
      # and gene status information -- if a loss/gain is defined at the arm
      # level then the focal call will be "arm_loss" or "arm_gain" respectively,
      # and so on.
      focal_call = case_when(focal_call == "loss, NA, NA" ~ "arm_loss",
                             focal_call == "neutral, uncallable, NA" ~ "uncallable",
                             focal_call == "neutral, loss, NA" ~ "cytoband_loss",
                             focal_call == "neutral, NA, NA" ~ "arm_neutral",
                             focal_call == "neutral, unstable, neutral" ~ "gene_neutral",
                             focal_call == "neutral, unstable, loss" ~ "gene_loss",
                             TRUE ~ "Other")
    )
} else {
  final_df <- consensus_seg_autosomes_df %>%
    mutate(chromosome_arm = gsub("(p|q).*", "\\1", cytoband)) %>%
    inner_join(arm_status_count, by = "chromosome_arm") %>%
    mutate(focal_call = dominant_status)
}

# Display final table
final_df %>%
  arrange(focal_call) %>%
  group_by(biospecimen_id, focal_call)
```

## Transform data into long format

### Tranform the chromosome arm status data

```{r}
if (!(running_in_ci)) {
  final_arm_status_df <- final_df %>%
    # Filter to only non-neutral chromosome arms
    filter(dominant_status.arm != "neutral") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = chromosome_arm,
      status = dominant_status.arm
    ) %>%
    distinct() %>%
    mutate(region_type = "chromosome_arm")
} else {
  final_arm_status_df <- final_df %>%
    # Filter to only non-neutral chromosome arms
    filter(dominant_status != "neutral") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = chromosome_arm,
      status = dominant_status
    ) %>%
    distinct() %>%
    mutate(region_type = "chromosome_arm")
}
```

### Transform the cytoband status data

```{r}
if (!(running_in_ci)) {
  final_cytoband_status_df <- final_df %>%
    # Filter to only neutral chromosome arms and cytobands
    # that are not NA
    filter(dominant_status.arm == "neutral",
           dominant_status.cytoband != "NA") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = cytoband,
      status = dominant_status.cytoband
    ) %>%
    distinct() %>%
    mutate(region_type = "cytoband")
}
```

### Transform the gene-level status data

```{r}
if (!(running_in_ci)) {
  final_gene_status_df <- final_df %>%
    # Filter to only neutral chromosome arms and cytoband
    # that are NA
    filter(dominant_status.arm == "neutral",
           dominant_status.cytoband == "NA") %>%
    select(
      Kids_First_Biospecimen_ID = biospecimen_id,
      region = gene_symbol,
      status = dominant_status
    ) %>%
    distinct() %>%
    mutate(region_type = "gene_symbol")
}
```

### Combine status data for all regions

```{r}
if (!(running_in_ci)) {
  # Bind the rows of each region's data.frame
  final_long_status_df <- bind_rows(final_arm_status_df,
                                    final_cytoband_status_df,
                                    final_gene_status_df) %>%
    filter(status != "uncallable")
} else {
  final_long_status_df <- final_arm_status_df %>%
    filter(status != "uncallable")
}

# Write final long status table to file
write_tsv(final_long_status_df, file.path(results_dir, "consensus_seg_most_focal_cn_status.tsv.gz"))

# Display final long status table
final_long_status_df
```

**Note:** There are no gene region status data in the final long status data.frame using the logic above.
There are also no gain status calls in this final data.frame.

### Spread status data

```{r}
final_wide_status_df <- final_long_status_df %>%
  tidyr::spread(Kids_First_Biospecimen_ID, status)

# Display status data spread across samples in wide format -- each row is a
# unique region
final_wide_status_df
```


## Session Info

```{r}
sessionInfo()
```
